Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks

Zuguang Li; Shaohua Wu; Liang Li; Songge Zhang

Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks

Machine Learning 2025-01-15 v2 Computation and Language Distributed, Parallel, and Cluster Computing

Authors: Zuguang Li , Shaohua Wu , Liang Li , Songge Zhang

Abstract

In this letter, we propose an energy-efficient split learning (SL) framework for fine-tuning large language models (LLMs) using geo-distributed personal data at the network edge, where LLMs are split and alternately across massive mobile devices and an edge server. Considering the device heterogeneity and channel dynamics in edge networks, a \underline{C}ut l\underline{A}yer and computing \underline{R}esource \underline{D}ecision (CARD) algorithm is developed to minimize training delay and energy consumption. Simulation results demonstrate that the proposed approach reduces the average training delay and server's energy consumption by 70.8% and 53.1%, compared to the benchmarks, respectively.

Keywords

large language model training large language model inference extreme learning machine

Cite

@article{arxiv.2412.00090,
  title  = {Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks},
  author = {Zuguang Li and Shaohua Wu and Liang Li and Songge Zhang},
  journal= {arXiv preprint arXiv:2412.00090},
  year   = {2025}
}

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5 pages, 6 figures

Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks

Abstract

Keywords

Cite

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